This chapter describes methods by which data can be classified. There are many methods which purport to classify data, and each one performs the classification in a different manner and typically with differing results. The variation in outcome can be explained by saying that the different mathematics associated with each method views the data from various different perspectives, assigning data to classifications that can, and usually are, different. A metaclassifier, however, is a method by which the results of these individual classifiers are considered as input to an ANN that forms the classifications based on the differing views and perspectives of the individual ANNs. In short, the different perspectives of the individual ANNs are brought together to produce a single, superior classification taking into account the various algorithms that produce certain views of the data. The MetaNet is developed in detail and shown to be better than any other metaclassifier ANN.
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